


'''simple docstring'''
import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCamelCase__          = 2_0_4_8
UpperCamelCase__          = 4_0_9_6
UpperCamelCase__          = 4_2
UpperCamelCase__          = os.environ.pop('''PROCESS_TRAIN''', '''false''')
UpperCamelCase__          = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}





def    a__			(   lowerCAmelCase__		)       ->      Optional[int]:
    def choose_first(lowerCAmelCase__    ,   lowerCAmelCase__=False		):
        assert isinstance(lowerCAmelCase__    ,   lowerCAmelCase__		)
        if len(lowerCAmelCase__		) == 1:
            UpperCAmelCase__     :						Optional[int]						=							answer[0]
            return {k: [answer[k]] for k in answer} if is_long_answer else answer
        for a in answer:
            if is_long_answer:
                UpperCAmelCase__     :						Tuple						=							{k: [a[k]] for k in a}
            if len(a['''start_token''']		) > 0:
                break
        return a

    UpperCAmelCase__     :						List[Any]						=							{'''id''': example['''id''']}
    UpperCAmelCase__     :						int						=							example['''annotations''']
    UpperCAmelCase__     :						Any						=							annotation['''yes_no_answer''']
    if 0 in yes_no_answer or 1 in yes_no_answer:
        UpperCAmelCase__     :						Union[str, Any]						=							['''yes'''] if 1 in yes_no_answer else ['''no''']
        UpperCAmelCase__     :						int						=							[]
        UpperCAmelCase__     :						Union[str, Any]						=							[]
        UpperCAmelCase__     :						Dict						=							['''<cls>''']
    else:
        UpperCAmelCase__     :						Tuple						=							['''short''']
        UpperCAmelCase__     :						Optional[Any]						=							choose_first(annotation['''short_answers''']		)
        if len(out['''start_token''']		) == 0:
            # answer will be long if short is not available
            UpperCAmelCase__     :						Dict						=							['''long''']
            UpperCAmelCase__     :						List[str]						=							choose_first(annotation['''long_answer''']    ,   is_long_answer=lowerCAmelCase__		)
            UpperCAmelCase__     :						str						=							[]
        answer.update(lowerCAmelCase__		)

    # disregard some samples
    if len(answer['''start_token''']		) > 1 or answer["start_token"] == answer["end_token"]:
        UpperCAmelCase__     :						Optional[int]						=							True
    else:
        UpperCAmelCase__     :						Any						=							False

    UpperCAmelCase__     :						str						=							['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
    if not all(isinstance(answer[k]    ,   lowerCAmelCase__		) for k in cols		):
        raise ValueError('''Issue in ID'''    ,   example['''id''']		)

    return answer





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      Optional[Any]:
    UpperCAmelCase__     :						Union[str, Any]						=							_get_single_answer(lowerCAmelCase__		)
    # bytes are of no use
    del answer["start_byte"]
    del answer["end_byte"]

    # handle yes_no answers explicitly
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        UpperCAmelCase__     :						int						=							example['''document''']['''tokens''']
        UpperCAmelCase__     :						str						=							[]
        for i in range(len(doc['''token''']		)		):
            if not doc["is_html"][i]:
                context.append(doc['''token'''][i]		)
        return {
            "context": " ".join(lowerCAmelCase__		),
            "answer": {
                "start_token": -1_00,  # ignore index in cross-entropy
                "end_token": -1_00,  # ignore index in cross-entropy
                "category": answer["category"],
                "span": answer["category"],  # extra
            },
        }

    # later, help in removing all no answers
    if answer["start_token"] == [-1]:
        return {
            "context": "None",
            "answer": {
                "start_token": -1,
                "end_token": -1,
                "category": "null",
                "span": "None",  # extra
            },
        }

    # handling normal samples

    UpperCAmelCase__     :						List[Any]						=							['''start_token''', '''end_token''']
    answer.update({k: answer[k][0] if len(answer[k]		) > 0 else answer[k] for k in cols}		)  # e.g. [10] == 10

    UpperCAmelCase__     :						str						=							example['''document''']['''tokens''']
    UpperCAmelCase__     :						List[Any]						=							answer['''start_token''']
    UpperCAmelCase__     :						int						=							answer['''end_token''']

    UpperCAmelCase__     :						Union[str, Any]						=							[]
    for i in range(len(doc['''token''']		)		):
        if not doc["is_html"][i]:
            context.append(doc['''token'''][i]		)
        else:
            if answer["start_token"] > i:
                start_token -= 1
            if answer["end_token"] > i:
                end_token -= 1
    UpperCAmelCase__     :						Any						=							''' '''.join(context[start_token:end_token]		)

    # checking above code
    if assertion:
        UpperCAmelCase__     :						Any						=							doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
        UpperCAmelCase__     :						Union[str, Any]						=							doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
        UpperCAmelCase__     :						str						=							''' '''.join([old[i] for i in range(len(lowerCAmelCase__		)		) if not is_html[i]]		)
        if new != old:
            print('''ID:'''    ,   example['''id''']		)
            print('''New:'''    ,   lowerCAmelCase__    ,   end='''\n'''		)
            print('''Old:'''    ,   lowerCAmelCase__    ,   end='''\n\n'''		)

    return {
        "context": " ".join(lowerCAmelCase__		),
        "answer": {
            "start_token": start_token,
            "end_token": end_token - 1,  # this makes it inclusive
            "category": answer["category"],  # either long or short
            "span": new,  # extra
        },
    }





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=20_48    ,   lowerCAmelCase__=40_96    ,   lowerCAmelCase__=True		)       ->      Dict:
    # overlap will be of doc_stride - q_len

    UpperCAmelCase__     :						Any						=							get_context_and_ans(lowerCAmelCase__    ,   assertion=lowerCAmelCase__		)
    UpperCAmelCase__     :						Union[str, Any]						=							out['''answer''']

    # later, removing these samples
    if answer["start_token"] == -1:
        return {
            "example_id": example["id"],
            "input_ids": [[-1]],
            "labels": {
                "start_token": [-1],
                "end_token": [-1],
                "category": ["null"],
            },
        }

    UpperCAmelCase__     :						str						=							tokenizer(example['''question''']['''text''']    ,   out['''context''']		).input_ids
    UpperCAmelCase__     :						Optional[Any]						=							input_ids.index(tokenizer.sep_token_id		) + 1

    # return yes/no
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        UpperCAmelCase__     :						List[Any]						=							[]
        UpperCAmelCase__     :						Optional[Any]						=							[]
        UpperCAmelCase__     :						int						=							input_ids[:q_len]
        UpperCAmelCase__     :						Any						=							range(lowerCAmelCase__    ,   len(lowerCAmelCase__		)    ,   max_length - doc_stride		)
        for i in doc_start_indices:
            UpperCAmelCase__     :						Tuple						=							i + max_length - q_len
            UpperCAmelCase__     :						Optional[Any]						=							input_ids[i:end_index]
            inputs.append(q_indices + slice		)
            category.append(answer['''category'''][0]		)
            if slice[-1] == tokenizer.sep_token_id:
                break

        return {
            "example_id": example["id"],
            "input_ids": inputs,
            "labels": {
                "start_token": [-1_00] * len(lowerCAmelCase__		),
                "end_token": [-1_00] * len(lowerCAmelCase__		),
                "category": category,
            },
        }

    UpperCAmelCase__     :						Any						=							out['''context'''].split()
    UpperCAmelCase__     :						Any						=							splitted_context[answer['''end_token''']]
    UpperCAmelCase__     :						Dict						=							len(
        tokenizer(
            ''' '''.join(splitted_context[: answer['''start_token''']]		)    ,   add_special_tokens=lowerCAmelCase__    ,   ).input_ids		)
    UpperCAmelCase__     :						Optional[Any]						=							len(
        tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]		)    ,   add_special_tokens=lowerCAmelCase__		).input_ids		)

    answer["start_token"] += q_len
    answer["end_token"] += q_len

    # fixing end token
    UpperCAmelCase__     :						Union[str, Any]						=							len(tokenizer(lowerCAmelCase__    ,   add_special_tokens=lowerCAmelCase__		).input_ids		)
    if num_sub_tokens > 1:
        answer["end_token"] += num_sub_tokens - 1

    UpperCAmelCase__     :						Dict						=							input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
    UpperCAmelCase__     :						List[Any]						=							answer['''start_token''']
    UpperCAmelCase__     :						Any						=							answer['''end_token''']

    if assertion:
        UpperCAmelCase__     :						List[Any]						=							tokenizer.decode(lowerCAmelCase__		)
        if answer["span"] != new:
            print('''ISSUE IN TOKENIZATION'''		)
            print('''OLD:'''    ,   answer['''span''']		)
            print('''NEW:'''    ,   lowerCAmelCase__    ,   end='''\n\n'''		)

    if len(lowerCAmelCase__		) <= max_length:
        return {
            "example_id": example["id"],
            "input_ids": [input_ids],
            "labels": {
                "start_token": [answer["start_token"]],
                "end_token": [answer["end_token"]],
                "category": answer["category"],
            },
        }

    UpperCAmelCase__     :						Optional[Any]						=							input_ids[:q_len]
    UpperCAmelCase__     :						Any						=							range(lowerCAmelCase__    ,   len(lowerCAmelCase__		)    ,   max_length - doc_stride		)

    UpperCAmelCase__     :						Any						=							[]
    UpperCAmelCase__     :						Union[str, Any]						=							[]
    UpperCAmelCase__     :						List[str]						=							[]
    UpperCAmelCase__     :						Optional[int]						=							[]  # null, yes, no, long, short
    for i in doc_start_indices:
        UpperCAmelCase__     :						List[str]						=							i + max_length - q_len
        UpperCAmelCase__     :						Optional[int]						=							input_ids[i:end_index]
        inputs.append(q_indices + slice		)
        assert len(inputs[-1]		) <= max_length, "Issue in truncating length"

        if start_token >= i and end_token <= end_index - 1:
            UpperCAmelCase__     :						List[Any]						=							start_token - i + q_len
            UpperCAmelCase__     :						List[str]						=							end_token - i + q_len
            answers_category.append(answer['''category'''][0]		)  # ["short"] -> "short"
        else:
            UpperCAmelCase__     :						Union[str, Any]						=							-1_00
            UpperCAmelCase__     :						Tuple						=							-1_00
            answers_category.append('''null'''		)
        UpperCAmelCase__     :						List[Any]						=							inputs[-1][start_token : end_token + 1]

        answers_start_token.append(lowerCAmelCase__		)
        answers_end_token.append(lowerCAmelCase__		)
        if assertion:
            if new != old and new != [tokenizer.cls_token_id]:
                print('''ISSUE in strided for ID:'''    ,   example['''id''']		)
                print('''New:'''    ,   tokenizer.decode(lowerCAmelCase__		)		)
                print('''Old:'''    ,   tokenizer.decode(lowerCAmelCase__		)    ,   end='''\n\n'''		)
        if slice[-1] == tokenizer.sep_token_id:
            break

    return {
        "example_id": example["id"],
        "input_ids": inputs,
        "labels": {
            "start_token": answers_start_token,
            "end_token": answers_end_token,
            "category": answers_category,
        },
    }





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=20_48    ,   lowerCAmelCase__=40_96    ,   lowerCAmelCase__=False		)       ->      int:
    UpperCAmelCase__     :						Any						=							get_strided_contexts_and_ans(
        lowerCAmelCase__    ,   lowerCAmelCase__    ,   doc_stride=lowerCAmelCase__    ,   max_length=lowerCAmelCase__    ,   assertion=lowerCAmelCase__    ,   )

    return example





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      str:
    with jsonlines.open(lowerCAmelCase__    ,   '''a'''		) as writer:
        for example in tqdm(lowerCAmelCase__    ,   total=len(lowerCAmelCase__		)    ,   desc='''Saving samples ... '''		):
            UpperCAmelCase__     :						Dict						=							example['''labels''']
            for ids, start, end, cat in zip(
                example['''input_ids''']    ,   labels['''start_token''']    ,   labels['''end_token''']    ,   labels['''category''']    ,   ):
                if start == -1 and end == -1:
                    continue  # leave waste samples with no answer
                if cat == "null" and np.random.rand() < 0.6:
                    continue  # removing 50 % samples
                writer.write(
                    {
                        '''input_ids''': ids,
                        '''start_token''': start,
                        '''end_token''': end,
                        '''category''': CATEGORY_MAPPING[cat],
                    }		)


if __name__ == "__main__":
  from datasets import load_dataset

  from transformers import BigBirdTokenizer

  UpperCamelCase__          = load_dataset('''natural_questions''')
  UpperCamelCase__          = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

  UpperCamelCase__          = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

  UpperCamelCase__          = {
      '''tokenizer''': tokenizer,
      '''doc_stride''': DOC_STRIDE,
      '''max_length''': MAX_LENGTH,
      '''assertion''': False,
  }
  UpperCamelCase__          = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
  UpperCamelCase__          = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
  print(data)

  np.random.seed(SEED)
  UpperCamelCase__          = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
  save_to_disk(data, file_name=cache_file_name)





'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




